B16: Genetic programming in feedback registers designing

In BIST structures feedback registers play the role of test generators and test response compactors. Linear feedback shift registers (LFSR) are here of predominating importance. These registers are relatively simple in designing. Non-linear feedback shift registers designing to diagnostic aims is considerably more complicated. The possibility to use the genetic programming to design the non-linear feedback shift registers is presented in the article. Usefulness of this approach to design the registers helpful in the BIST structures is testified by numerous examples.Badura D. (1992). Techniki projektowania samotestowalnych układów i pakietów cyfrowych wykorzystujące rejestry szeregowe z nieliniowym sprzężeniem zwrotnym, Uniwersytet Śląski.Elmrych M. (2004). Metody genetyczne w projektowaniu rejestrów z nieliniowym sprzężeniem zwrotnym, Uniwersytet Śląski, Sosnowiec.Gościniak I. (1996). Liniowa metoda zwiększania efektywności diagnostycznej ścieżki samotestującej i pierścienia samotestującego. Pomiary Automatyka Kontrola, nr 4, Warszawa.Gościniak I., Chodacki M. (2003). Genetic algorithms for the designing feedback shift registers, 6 th IEEE International Workshop on Design and Diagnostics of Electronic Circuits and Systems, Poznań, Poland, pp 301-302.Hławiczka A. (1997). Rejestry liniowe – analiza, synteza i zastosowania w testowaniu układów cyfrowych, Wydawnictwo Politechniki Śląskiej, Gliwice.Hürner H. (1996). A C++ Class Library for Genetic Programming, The Vienna University of Economics.Koza J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.Słota T. (2004). Metody genetyczne w projektowaniu rejestrów liczących, Uniwersytet Śląski, Sosnowiec.Wagner F. (1977). Projektowanie krótkich rejestrów liczących, Politechnika Śląska, ZN Nr. 526

Artificial intelligence makes computers lazy

This paper looks at the age-old problem of trying to instil some degree of intelligence in computers. Genetic Algorithms (GA) and Genetic Programming (GP) are techniques that are used to evolve a solution to a problem using processes that mimic natural evolution. This paper reflects on the experience gained while conducting research applying GA and GP to two quite different problems: Medical Diagnosis and Robot Path Planning. An observation is made that when these algorithms are not applied correctly the computer seemingly exhibits lazy behaviour, arriving at a suboptimal solutions. Using examples, this paper shows how this 'lazy' behaviour can be overcome

XML-based genetic rules for scene boundary detection in a parallel processing environment

Genetic programming is based on Darwinian evolutionary theory that suggests that the best solution for a problem can be evolved by methods of natural selection of the fittest organisms in a population. These principles are translated into genetic programming by populating the solution space with an initial number of computer programs that can possibly solve the problem and then evolving the programs by means of mutation, reproduction and crossover until a candidate solution can be found that is close to or is the optimal solution for the problem. The computer programs are not fully formed source code but rather a derivative that is represented as a parse tree. The initial solutions are randomly generated and set to a certain population size that the system can compute efficiently. Research has shown that better solutions can be obtained if 1) the population size is increased and 2) if multiple runs are performed of each experiment. If multiple runs are initiated on many machines the probability of finding an optimal solution are increased exponentially and computed more efficiently. With the proliferation of the web and high speed bandwidth connections genetic programming can take advantage of grid computing to both increase population size and increasing the number of runs by utilising machines connected to the web. Using XML-Schema as a global referencing mechanism for defining the parameters and syntax of the evolvable computer programs all machines can synchronise ad-hoc to the ever changing environment of the solution space. Another advantage of using XML is that rules are constructed that can be transformed by XSLT or DOM tree viewers so they can be understood by the GP programmer. This allows the programmer to experiment by manipulating rules to increase the fitness of a rule and evaluate the selection of parameters used to define a solution

Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool

[EN] Traversal-based automated software testing involves testing an application via its graphical user interface (GUI) and thereby taking the user's point of view and executing actions in a human-like manner. These actions are decided on the fly, as the software under test (SUT) is being run, as opposed to being set up in the form of a sequence prior to the testing, a sequence that is then used to exercise the SUT. In practice, random choice is commonly used to decide which action to execute at each state (a procedure commonly referred to as monkey testing), but a number of alternative mechanisms have also been proposed in the literature. Here we propose using genetic programming (GP) to evolve such an action selection strategy, defined as a list of IF-THEN rules. Genetic programming has proved to be suited for evolving all sorts of programs, and rules in particular, provided adequate primitives (functions and terminals) are defined. These primitives must aim to extract the most relevant information from the SUT and the dynamics of the testing process. We introduce a number of such primitives suited to the problem at hand and evaluate their usefulness based on various metrics. We carry out experiments and compare the results with those obtained by random selection and also by Q-learning, a reinforcement learning technique. Three applications are used as Software Under Test (SUT) in the experiments. The analysis shows the potential of GP to evolve action selection strategies.Esparcia Alcázar, AI.; Almenar-Pedrós, F.; Vos, TE.; Rueda Molina, U. (2018). Using genetic programming to evolve action selection rules in traversal-based automated software testing: results obtained with the TESTAR tool. Memetic Computing. 10(3):257-265. https://doi.org/10.1007/s12293-018-0263-8S257265103Aho P, Menz N, Rty T (2013) Dynamic reverse engineering of GUI models for testing. In: Proceedings of 2013 international conference on control, decision and information technologies (CoDIT’13)Aho P, Oliveira R, Algroth E, Vos T (2016) Evolution of automated testing of software systems through graphical user interface. In: Procs. of the 1st international conference on advances in computation, communications and services (ACCSE 2016), Valencia, pp 16–21Alegroth E, Feldt R, Ryrholm L (2014) Visual GUI testing in practice: challenges, problems and limitations. Empir Softw Eng 20:694–744. https://doi.org/10.1007/s10664-013-9293-5Barr ET, Harman M, McMinn P, Shahbaz M, Yoo S (2015) The oracle problem in software testing: a survey. IEEE Trans Softw Eng 41(5):507–525Bauersfeld S, Vos TEJ (2012) A reinforcement learning approach to automated GUI robustness testing. In: Fast abstracts of the 4th symposium on search-based software engineering (SSBSE 2012), pp 7–12Bauersfeld S, de Rojas A, Vos T (2014) Evaluating rogue user testing in industry: an experience report. In: 2014 IEEE eighth international conference on research challenges in information science (RCIS), pp 1–10. https://doi.org/10.1109/RCIS.2014.6861051Bauersfeld S, Vos TEJ, Condori-Fernández N, Bagnato A, Brosse E (2014) Evaluating the TESTAR tool in an industrial case study. In: 2014 ACM-IEEE international symposium on empirical software engineering and measurement, ESEM 2014, Torino, Italy, September 18–19, 2014, p 4Bauersfeld S, Wappler S, Wegener J (2011) A metaheuristic approach to test sequence generation for applications with a GUI. In: Cohen MB, Ó Cinnéide M (eds) Search based software engineering: third international symposium, SSBSE 2011, Szeged, Hungary, September 10-12, 2011. Proceedings. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 173–187Brameier MF, Banzhaf W (2010) Linear genetic programming, 1st edn. Springer, New YorkChaudhary N, Sangwan O (2016) Metrics for event driven software. Int J Adv Comput Sci Appl 7(1):85–89Esparcia-Alcázar AI, Almenar F, Martínez M, Rueda U, Vos TE (2016) Q-learning strategies for action selection in the TESTAR automated testing tool. In: Proceedings of META 2016 6th international conference on metaheuristics and nature inspired computing, pp 174–180Esparcia-Alcázar AI, Almenar F, Rueda U, Vos TEJ (2017) Evolving rules for action selection in automated testing via genetic programming–a first approach. In: Squillero G, Sim K (eds) Applications of evolutionary computation: 20th European conference, evoapplications 2017, Amsterdam, The Netherlands, April 19–21, 2017, Proceedings, part II. Springer, pp 82–95. https://doi.org/10.1007/978-3-319-55792-2_6Esparcia-Alcázar AI, Moravec J (2013) Fitness approximation for bot evolution in genetic programming. Soft Comput 17(8):1479–1487. https://doi.org/10.1007/s00500-012-0965-7He W, Zhao R, Zhu Q (2015) Integrating evolutionary testing with reinforcement learning for automated test generation of object-oriented software. Chin J Electron 24(1):38–45Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, CambridgeLehman J, Stanley KO (2011) Novelty search and the problem with objectives. In: Riolo R, Vladislavleva E, Moore JH (eds) Genetic programming theory and practice IX, genetic and evolutionary computation. Springer, New York, pp 37–56Memon AM, Soffa ML, Pollack ME (2001) Coverage criteria for GUI testing. In: Proceedings of ESEC/FSE 2001, pp 256–267Rueda U, Vos TEJ, Almenar F, Martínez MO, Esparcia-Alcázar AI (2015) TESTAR: from academic prototype towards an industry-ready tool for automated testing at the user interface level. In: Canos JH, Gonzalez Harbour M (eds) Actas de las XX Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2015), pp 236–245Seesing A, Gross HG (2006) A genetic programming approach to automated test generation for object-oriented software. Int Trans Syst Sci Appl 1(2):127–134Vos TE, Kruse PM, Condori-Fernández N, Bauersfeld S, Wegener J (2015) TESTAR: tool support for test automation at the user interface level. Int J Inf Syst Model Des 6(3):46–83. https://doi.org/10.4018/IJISMD.2015070103Wappler S, Wegener J (2006) Evolutionary unit testing of object-oriented software using strongly-typed genetic programming. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO’06. ACM, New York, NY, USA, pp 1925–1932. URL https://doi.org/10.1145/1143997.1144317Watkins C (1989) Learning from delayed rewards. Ph.D. Thesis. Cambridge Universit

Temporal Feature Selection with Symbolic Regression

Building and discovering useful features when constructing machine learning models is the central task for the machine learning practitioner. Good features are useful not only in increasing the predictive power of a model but also in illuminating the underlying drivers of a target variable. In this research we propose a novel feature learning technique in which Symbolic regression is endowed with a ``Range Terminal\u27\u27 that allows it to explore functions of the aggregate of variables over time. We test the Range Terminal on a synthetic data set and a real world data in which we predict seasonal greenness using satellite derived temperature and snow data over a portion of the Arctic. On the synthetic data set we find Symbolic regression with the Range Terminal outperforms standard Symbolic regression and Lasso regression. On the Arctic data set we find it outperforms standard Symbolic regression, fails to beat the Lasso regression, but finds useful features describing the interaction between Land Surface Temperature, Snow, and seasonal vegetative growth in the Arctic